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Enhancing Prediction of Next Points of Interest Using Self-Supervised Contrastive Learning
https://ipsj.ixsq.nii.ac.jp/records/236340
https://ipsj.ixsq.nii.ac.jp/records/236340926c0ae7-98d9-4c9a-94d7-d4369a1085a3
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
IPSJ-Z86-7D-05.pdf (212.0 kB)
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Copyright (c) 2024 by the Information Processing Society of Japan
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| Item type | National Convention(1) | |||||||||||
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| 公開日 | 2024-03-01 | |||||||||||
| タイトル | ||||||||||||
| タイトル | Enhancing Prediction of Next Points of Interest Using Self-Supervised Contrastive Learning | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | ネットワーク | |||||||||||
| 資源タイプ | ||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||||||||
| 資源タイプ | conference paper | |||||||||||
| 著者所属 | ||||||||||||
| 東工大 | ||||||||||||
| 著者所属 | ||||||||||||
| 関東学院大 | ||||||||||||
| 著者所属 | ||||||||||||
| 東工大 | ||||||||||||
| 著者名 |
韓, 秋涵
× 韓, 秋涵
× 吉川, 厚
× 山村, 雅幸
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| 論文抄録 | ||||||||||||
| 内容記述タイプ | Other | |||||||||||
| 内容記述 | In recent years, the ubiquitous GPS-equipped mobile devices have substantially advanced the research on personalized user mobility patterns through location data. However, accurately predicting Points of Interest (POIs) remains challenging in scenarios with sparse data. This study addresses such challenges by employing a self-supervised contrastive learning approach, which utilizes the adjacency matrices of locations within cities to predict users' subsequent POIs. This method not only improves prediction accuracy where data is limited but also sets the stage for innovative applications in deciphering human mobility and enhancing personalized services. | |||||||||||
| 書誌レコードID | ||||||||||||
| 収録物識別子タイプ | NCID | |||||||||||
| 収録物識別子 | AN00349328 | |||||||||||
| 書誌情報 |
第86回全国大会講演論文集 巻 2024, 号 1, p. 45-46, 発行日 2024-03-01 |
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| 出版者 | ||||||||||||
| 言語 | ja | |||||||||||
| 出版者 | 情報処理学会 | |||||||||||
